Skip to main content
SpringerLink
Log in

Comparative large-scale evaluation of human and active appearance model based tracking performance of anatomical landmarks in X-ray locomotion sequences

  • Applied Problems
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

The detailed understanding of animal locomotion is an important part of biology, motion science and robotics. To analyze the motion, high-speed x-ray sequences of walking animals are recorded. The biological evaluation is based on anatomical key points in the images, and the goal is to find these landmarks automatically. Unfortunately, low contrast and occlusions in the images drastically complicate this task. As recently shown, Active Appearance Models (AAMs) can be successfully applied to this problem. However, obtaining reliable quantitative results is a tedious task, as the human error is unknown. In this work, we present the results of a large scale study which allows us to quantify both the tracking performance of humans as well as AAMs. Furthermore, we show that the AAM-based approach provides results which are comparable to those of human experts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. M. Gatesy, “Guineafowl hind limb function. I: Cineradiographic analysis and speed effects,” J. Morphol. 240(2), 1097–4687 (1999).

    Article  Google Scholar 

  2. S. M. Gatesy, D. B. Baier, F. A. Jenkins, and K. P. Dial, “Scientific rotoscoping: A morphology-based method of 3-d motion analysis and visualization,” Journal of Experimental Zoology Part A: Ecological Genetics and Physiology A 313(5), 244–261 (2010).

    Google Scholar 

  3. E. L. Brainerd, D. B. Baier, S. M. Gatesy, T. L. Hedrick, K. A. Metzger, S. L. Gilbert, and J. J. Crisco, “X-ray reconstruction of moving morphology (XROMM): Precision, accuracy and applications in comparative biomechanics research,” J. Exp. Zool. A 313A(5), 262–279 (2010).

    Google Scholar 

  4. M. Fischer and K. Lilje, Dogs in Motion, VDH, 2011.

    Google Scholar 

  5. B. K. P. Horn and B. G. Schunck, “Determining optical flow,” Artif. Intell. 17(1–3), 185–203 (1981).

    Article  Google Scholar 

  6. S. Baker and I. Matthews, “Lucas-kanade 20 years on: A unifying framework,” Int. J. Comput. Vision 56(3), 221–255, 2004.

    Article  Google Scholar 

  7. G. D. Hager and P. N. Belhumeur, “Efficient region tracking with parametric models of geometry and illumination,” IEEE T. Pattern Anal. 20(10), 1025–1039 (1998).

    Article  Google Scholar 

  8. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vision 60(2), 91–110 (2004).

    Article  Google Scholar 

  9. D. Haase and J. Denzler, “Anatomical landmark tracking for the analysis of animal locomotion in X-ray videos using active appearance models,” in Proceedings of the 17th Scandinavian Conference on Image Analysis (SCIA 2011), ser. LNCS, Eds. by A. Heyden and F. Kahl (Springer, 2011), No. 6688, pp. 604–615.

    Google Scholar 

  10. T. F. Cootes, G. J. Edwards, and C. J. Taylor, “Active appearance models,” in Proceedings of the 5th European Conference on Computer Vision, ser. LNCS, Eds. by H. Burkhardt and B. Neumann (Springer, 1998), Vol. 1407, pp. 484–498.

    Google Scholar 

  11. G. J. Edwards, T. F. Cootes, and C. J. Taylor, “Face recognition using active appearance models,” in Proceedings of the 5th European Conference on Computer Vision, ser. LNCS, Eds. by H. Burkhardt and B. Neumann (Springer, 1998), Vol. 1407, pp. 581–595.

    Google Scholar 

  12. T. F. Cootes, G. J. Edwards, and C. J. Taylor, “Active appearance models,” IEEE T. Pattern Anal. 23(6), 681–685 (2001).

    Article  Google Scholar 

  13. D. Haase, J. A. Nyakatura, and J. Denzler, “Multi-view active appearance models for the X-ray based analysis of avian bipedal locomotion,” in Proceedings of the 33rd DAGM Symposium (DAGM 2011), ser. LNCS, Eds. by R. Mester and M. Felsberg (Springer, 2011), No. 6835, pp. 11–20.

    Google Scholar 

  14. F. L. Bookstein, “Landmark methods for forms without landmarks: Morphometrics of group differences in outline shape,” Med. Image Anal. 1(3), 225–243 (1997).

    Article  Google Scholar 

  15. I. Matthews and S. Baker, “Active appearance models revisited,” International Journal of Computer Vision 60(2), 135–164 (2004).

    Article  Google Scholar 

  16. B. Lelieveldt, M. üzümcü, R. van der Geest, J. Reiber, and M. Sonka, “Multi-view active appearance models for consistent segmentation of multiple standard views,” International Congress Series 1256, 1141–1146 (2003).

    Article  Google Scholar 

  17. E. Oost, G. Koning, M. Sonka, P. V. Oemrawsingh, J. H. C. Reiber, and B. P. F. Lelieveldt, “Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic programming,” IEEE T. Med. Imaging 25(9), 1158–1171 (2006).

    Article  Google Scholar 

  18. M. B. Stegmann, “Active appearance models: theory, extensions and cases,” in Master’s Thesis (Technical University of Denmark, DTU, 2000).

    Google Scholar 

  19. D. Haase and J. Denzler, “Comparative evaluation of human and active appearance model based tracking performance of anatomical landmarks in locomotion analysis,” in Proceedings of the 8th Open German-Russian Workshop Pattern Recognition and Image Understanding (OGRW-8-2011), 2011, pp. 96–99.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Vision Friedrich Schiller University of Jena, Ernst-Abbe-Platz 2-4, 07743, Jena, Germany

    D. Haase

  2. Institute of Systematic Zoology and Evolutionary Biology with Phyletic Museum, Friedrich Schiller University of Jena, Erbertstr. 1, 07743, Jena, Germany

    J. A. Nyakatura

Authors
  1. D. Haase
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. A. Nyakatura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Denzler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Haase.

Additional information

The article is published in the original. This article uses the materials of the report submitted at the 8th Open German-Russian Workshop “Pattern Recognition and Image Understanding,” Nizhni Novgorod, November 21–26, 2011.

Daniel Haase Daniel Haase received the Diploma degree in computer science with honors from the Friedrich Schiller University of Jena, Germany, in April 2010. Currently, he is working towards his Ph.D. at the chair for computer vision at the University of Jena under the supervision of Joachim Denzler. His research interest include statistical modeling, 2D/3D landmark tracking, 3D reconstruction and machine learning.

Joachim Denzler Joachim Denzler, born April 16th 1967, earned the degrees “Diplom-Informatiker”, “Dr.-Ing.”, and “Habilitation”” from the University of Erlangen in the years 1992, 1997, and 2003, respectively. Currently, he holds a position of full professor for computer science and is head of the Chair for Computer Vision, Department of Mathematics and Computer Science, Friedrich Schiller University of Jena. His research interests comprise active computer vision, object recognition and tracking, 3D reconstruction, and plenoptic modeling, as well as computer vision for autonomous systems. He is author and coauthor of over 200 journal papers and technical articles. He is a member of the IEEE, IEEE computer society, DAGM, and GI. For his work on object tracking, plenoptic modeling, and active object recognition and state estimation, he was awarded the DAGM best paper awards in 1996, 1999, and 2001, respectively.

John A. Nyakatura received his Ph.D. in the field of Evolutionary Biology / Zoology under the supervision of Prof. Dr. Martin S. Fischer with the distinction of “summa cum laude” from the University of Jena in 2011. He is currently participating in several research projects concerned with the evolution and functional morphology of locomotor systems of vertebrates. His research is at the interface of anatomy and biomechanics and combines diverse experimental and classic anatomical approaches from an evolutionary perspective. In collaborations, his work bridges gaps to the fields of robotics, paleontology and computer vision.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Haase, D., Nyakatura, J.A. & Denzler, J. Comparative large-scale evaluation of human and active appearance model based tracking performance of anatomical landmarks in X-ray locomotion sequences. Pattern Recognit. Image Anal. 24, 86–92 (2014). https://doi.org/10.1134/S1054661814010222

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661814010222

Keywords

Search

Navigation